#include <Eigen/Array>

int
main(int argc, char* argv[])
{
	std::cout.precision(2);

	// demo static functions
	Eigen::Matrix3f m3 = Eigen::Matrix3f::Random();
	Eigen::Matrix4f m4 = Eigen::Matrix4f::Identity();

	std::cout << "*** Step 1 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;

	// demo non-static set... functions
	m4.setZero();
	m3.diagonal().setOnes();

	std::cout << "*** Step 2 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;

	// demo fixed-size block() expression as lvalue and as rvalue
	m4.block<3, 3>(0, 1) = m3;
	m3.row(2) = m4.block<1, 3>(2, 0);

	std::cout << "*** Step 3 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;

	// demo dynamic-size block()
	{
		int rows = 3, cols = 3;
		m4.block(0, 1, 3, 3).setIdentity();
		std::cout << "*** Step 4 ***\nm4:\n" << m4 << std::endl;
	}

	// demo vector blocks
	m4.diagonal().block(1, 2).setOnes();
	std::cout << "*** Step 5 ***\nm4.diagonal():\n" << m4.diagonal() << std::endl;
	std::cout << "m4.diagonal().start(3)\n" << m4.diagonal().start(3) << std::endl;

	// demo coeff-wise operations
	m4 = m4.cwise() * m4;
	m3 = m3.cwise().cos();
	std::cout << "*** Step 6 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;

	// sums of coefficients
	std::cout << "*** Step 7 ***\n m4.sum(): " << m4.sum() << std::endl;
	std::cout << "m4.col(2).sum(): " << m4.col(2).sum() << std::endl;
	std::cout << "m4.colwise().sum():\n" << m4.colwise().sum() << std::endl;
	std::cout << "m4.rowwise().sum():\n" << m4.rowwise().sum() << std::endl;

	// demo intelligent auto-evaluation
	m4 = m4 * m4;							  // auto-evaluates so no aliasing problem (performance penalty is low)
	Eigen::Matrix4f other = (m4 * m4).lazy(); // forces lazy evaluation
	m4 = m4 + m4;							  // here Eigen goes for lazy evaluation, as with most expressions
	m4 = -m4 + m4 + 5 * m4;					  // same here, Eigen chooses lazy evaluation for all that.
	m4 = m4 * (m4 + m4);					  // here Eigen chooses to first evaluate m4 + m4 into a temporary.
											  // indeed, here it is an optimization to cache this intermediate result.
	m3 = m3 * m4.block<3, 3>(1, 1);			  // here Eigen chooses NOT to evaluate block() into a temporary
									// because accessing coefficients of that block expression is not more costly than
									// accessing coefficients of a plain matrix.
	m4 = m4 * m4.transpose();		 // same here, lazy evaluation of the transpose.
	m4 = m4 * m4.transpose().eval(); // forces immediate evaluation of the transpose

	std::cout << "*** Step 8 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;
}
